A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions

Descriptive data mining has been widely applied in hydrology as the regionalisation algorithms to identify the statistically homogeneous rainfall regions. However, previous studies employed regionalisation algorithms, namely agglomerative hierarchical and non-hierarchical regionalisation algorithms...

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Main Authors: Zun, Liang Chuan, Wan Yusof, Wan Nur Syahidah, Senawi, Azlyna, Mohd Akramin, Mohd Romlay, Soo, Fen Fam, Wendy, Ling Shinyie, Tan, Lit Ken
Format: Article
Language:English
Published: Universiti Putra Malaysia Press 2022
Online Access:http://psasir.upm.edu.my/id/eprint/98159/1/18%20JST-2029-2020
http://psasir.upm.edu.my/id/eprint/98159/
http://www.pertanika.upm.edu.my/pjst/browse/regular-issue?article=JST-2029-2020
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spelling my.upm.eprints.981592022-08-13T00:37:18Z http://psasir.upm.edu.my/id/eprint/98159/ A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions Zun, Liang Chuan Wan Yusof, Wan Nur Syahidah Senawi, Azlyna Mohd Akramin, Mohd Romlay Soo, Fen Fam Wendy, Ling Shinyie Tan, Lit Ken Descriptive data mining has been widely applied in hydrology as the regionalisation algorithms to identify the statistically homogeneous rainfall regions. However, previous studies employed regionalisation algorithms, namely agglomerative hierarchical and non-hierarchical regionalisation algorithms requiring post-processing techniques to validate and interpret the analysis results. The main objective of this study is to investigate the effectiveness of the automated agglomerative hierarchical and non-hierarchical regionalisation algorithms in identifying the homogeneous rainfall regions based on a new statistically significant difference regionalised feature set. To pursue this objective, this study collected 20 historical monthly rainfall time-series data from the rain gauge stations located in the Kuantan district. In practice, these 20 rain gauge stations can be categorised into two statistically homogeneous rainfall regions, namely distinct spatial and temporal variability in the rainfall amounts. The results of the analysis show that Forgy K-means non-hierarchical (FKNH), HartiganWong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. Furthermore, FKNH, HKNH, and LKNH yielded the highest regionalisation accuracy compared to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. Based on the regionalisation results yielded in this study, the reliability and accuracy that assessed the risk of extreme hydro-meteorological events for the Kuantan district can be improved. In particular, the regional quantile estimates can provide a more accurate estimation compared to at-site quantile estimates using an appropriate statistical distribution. Universiti Putra Malaysia Press 2022 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/98159/1/18%20JST-2029-2020 Zun, Liang Chuan and Wan Yusof, Wan Nur Syahidah and Senawi, Azlyna and Mohd Akramin, Mohd Romlay and Soo, Fen Fam and Wendy, Ling Shinyie and Tan, Lit Ken (2022) A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions. Pertanika Journal of Science & Technology, 30 (1). pp. 319-342. ISSN 0128-7680; ESSN: 2231-8526 http://www.pertanika.upm.edu.my/pjst/browse/regular-issue?article=JST-2029-2020 10.47836/pjst.30.1.18
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Descriptive data mining has been widely applied in hydrology as the regionalisation algorithms to identify the statistically homogeneous rainfall regions. However, previous studies employed regionalisation algorithms, namely agglomerative hierarchical and non-hierarchical regionalisation algorithms requiring post-processing techniques to validate and interpret the analysis results. The main objective of this study is to investigate the effectiveness of the automated agglomerative hierarchical and non-hierarchical regionalisation algorithms in identifying the homogeneous rainfall regions based on a new statistically significant difference regionalised feature set. To pursue this objective, this study collected 20 historical monthly rainfall time-series data from the rain gauge stations located in the Kuantan district. In practice, these 20 rain gauge stations can be categorised into two statistically homogeneous rainfall regions, namely distinct spatial and temporal variability in the rainfall amounts. The results of the analysis show that Forgy K-means non-hierarchical (FKNH), HartiganWong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. Furthermore, FKNH, HKNH, and LKNH yielded the highest regionalisation accuracy compared to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. Based on the regionalisation results yielded in this study, the reliability and accuracy that assessed the risk of extreme hydro-meteorological events for the Kuantan district can be improved. In particular, the regional quantile estimates can provide a more accurate estimation compared to at-site quantile estimates using an appropriate statistical distribution.
format Article
author Zun, Liang Chuan
Wan Yusof, Wan Nur Syahidah
Senawi, Azlyna
Mohd Akramin, Mohd Romlay
Soo, Fen Fam
Wendy, Ling Shinyie
Tan, Lit Ken
spellingShingle Zun, Liang Chuan
Wan Yusof, Wan Nur Syahidah
Senawi, Azlyna
Mohd Akramin, Mohd Romlay
Soo, Fen Fam
Wendy, Ling Shinyie
Tan, Lit Ken
A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions
author_facet Zun, Liang Chuan
Wan Yusof, Wan Nur Syahidah
Senawi, Azlyna
Mohd Akramin, Mohd Romlay
Soo, Fen Fam
Wendy, Ling Shinyie
Tan, Lit Ken
author_sort Zun, Liang Chuan
title A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions
title_short A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions
title_full A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions
title_fullStr A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions
title_full_unstemmed A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions
title_sort comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions
publisher Universiti Putra Malaysia Press
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/98159/1/18%20JST-2029-2020
http://psasir.upm.edu.my/id/eprint/98159/
http://www.pertanika.upm.edu.my/pjst/browse/regular-issue?article=JST-2029-2020
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score 13.211869